ROLE OF AI IN RENEWABLE ENERGY

AI or Artificial Intelligence, You must be hearing this word on a daily basis in your everyday life . This word has picked up pace as generative AI models such as CHATGPT are gaining constant popularity, but there’s more to AI rather than just generating images, design, codes and content which makes daily lives easier. Until now generative models weren’t popular, the main reason being availability of a certain threshold of unique data. When a certain limit of data has been collected and processed in the system. This model learns the patterns and which helps them predicting or generating human-like responses to the given task.

Concludingly saying when we crossed the threshold of these data collections we unlocked millions of possibilities. Through this data we can run descriptive diagnostic predictive prescriptive models to find out the possible outcomes , problems happened , why it happened and solutions based on the problems.

How AI could be integrated with Renewable Energy ?

The AI integration with renewable energy will be the most fruitful innovation to nature till now, As integrating AI with wind turbines and hydro turbines will result in maximum power generation as weather forecasting has never been more accurate then its current capacity. By the help of google and deepmind we can determine the directions and force of the winds before 36 hours of generation which helps us to plan maintenance and can provide solutions accordingly. Thus by maintaining a healthy generating cycle.

The AI can be automated with renewable energy in mainly two ways

1. Fully automated decision
2. Aided decision making

Fully automated decision

The computer systems process information without human interference , henceforth creating opportunities for them to do complex tasks which can be handled faster and efficiently than humans would be able to handle in the same amount of time. For example : An automated AI systems for the schedule of monthly maintenance of a solar photovoltaic (PV) plant

The main constraint of fully automated decisions can be distorted by different kinds of bias, which may be the reason for multiple outcomes and leading the AI into making wrongful decision making.

Aided Decision Making

In the process of aided decision making AI systems generate the outcomes and the end decision will be human based and gives flexibility to take decisions based on various factors. Hence giving a chance to AI to learn from human based decisions for further complex decisions. While aided decision also free ups the human brain of researching the analyzing the data and only the end decision will be in human hand For example

Possible Problems During AI Integration

When we are transitioning from a technology that is being used at a very large scale there will be consequences of replacing. Here are some of problem by public and private enterprises and companies are as follows

Carbon emission : The international energy agency’s report, global CO2 emissions climbed to the highest in history, with an annual growth rate of 6%. The global growth in energy demand was cited as one of the primary reasons for such an increase. Although there is no one size fits all solution to reduce co2 emissions , Artificial Intelligence in energy can help develop a cleaner production process, enhance monitoring and standard compliance for fossils and create targeted mitigation strategies.

High Centralization: Global energy network relies more on major grids and centralized providers than ever before. Such a centralization at a high scale poses several challenges for sustainable development. Developing a smaller and interjoined network that is powered by AI is a go to option for reducing reliability on central utilities.

Smooth Transition to renewables : The market share of renewables in power generation will substantially increase from 44% to a highly optimistic 80% by 2050. Solar and wind farms are witnessing ground-breaking innovations and massive investments, hence to assure smooth operations AI can really be a crucial element for its success. AI helps with real-time monitoring of power grids, improves predictions of power fluctuations, and aids in developing new strategies for working with geothermal energy sources.

Benefits of AI in the Energy Sector

The use of AI in the energy industry has challenges, but the benefits are greater than the costs of implementation. Some examples of how AI can be used in the energy sector are smart grids, digitizing data, forecasting, and better resource management.

Data digitization : The energy sector has been digitizing at a rapid pace in recent years. AI has played a crucial role in this process. Deloitte states that 66% of oil and gas companies put a spotlight on the benefits of digitalization outweigh any cybersecurity risks. Artificial intelligence can assist in the transformation of energy companies by automating the collection of grid data and implementing frameworks for analysis. Hence these massive information can be integrated with AI and Machine learning algorithms for empowering the bounded options of sustainable development.

Smart Forecasting : Forecasting is used everywhere nowadays with the integration of embedded systems which collects the data which helps us accurately predict the generation of energy in particular geographical areas precisely. Deep learning AI algorithms can project data more accurately than all industry specialists .

Resource Management : In the energy and utilities sector, artificial intelligence depends on controlling, sustaining, and supplying uninterrupted power output. With AI power resource management, suppliers can balance traditional and renewable energy proportions. Resource management can also tune the grid optimal use or request maintenance in timely situations.

Failure Prevention: In the last few years, There have been dozens of ill-reputed energy-linked cases that have become more and more public whether it is oil spills or hazardous coal extraction facilities. AI development in failure prediction is a high priority in the industry. The monitoring data of patterns and trends , AI will identify problems before they happen. This ultimately will allow taking corrective action to avoid disruptions. The Modern AI development in the energy sector to utilize SCADA, maintenance and budget data to prevent shortages or grid failures

Predictive analysis for renewables : The analysis for renewables includes identifying areas with the highest potential for AI in renewable energy development like wind and solar panels. This analysis will be helpful on the given subject matter with this supplier would be able to use AI integration in the energy sector efficiently.

Common energy-related AI use cases

The most ambitious energy projects today are focused on a smart grid, energy-efficiency programs, digital twins, and renewable energy integration. These cutting-edge projects are powered by AI, Cloud, and digital technologies, which enable control and self-regulation for more efficient and responsive networks.

Smart grid

The smart grid is a revolutionary approach to energy efficiency networks that leverages the two-way flow of electricity and data. By using AI and digital technologies, the smart grid provides preventive and predictive maintenance, ensuring the grid's high functionality, resilience, and security. For instance, the collaboration between London's National Grid and IBM's cloud-based analytics is a prime example of the smart grid's capabilities.

Energy-efficiency programs

Energy efficiency is a crucial aspect of the Sustainable Development Goals, and AI-powered energy efficiency programs are an effective way to achieve them. These programs monitor energy usage, forecast energy consumption, and regulate usage during peak hours. Predictive analytics and Machine Learning provide precise forecasts that help design and implement energy efficiency plans at the company, municipality, or state levels.

Smart heaters

Smart heaters will be a part of the modern revolution of renewable strategies and their ability to control the entire heating system . By allocating power efficiently, smart heaters direct unused energy to specific areas, making them an essential part of renewable energy integration. For example, the N-iX team created an end-to-end signup flow for smart heaters, which allowed users to choose the smart heater and create a request to analyze the house by an expert. The team used Scala, React.js, and AWS to develop the solution, which yielded an MVP released to production and additional features.

Digital twins

Digital twins are a life-saving framework for the industrial energy complex. These multi-dimensional visual representations of processes, facilities, or physical objects act like real-time virtual models that present more research possibilities than simulations. Digital twins provide a step forward in better servicing, experimenting, maintaining, and optimizing the energy network, either traditional or renewable.

Renewable energy integration

Balancing traditional and renewable energy sources is crucial for major energy providers, and AI in energy and Machine Learning makes it possible to forecast and predict the best circumstances for accurate integration of renewables. We confidently forecast the energy outputs of wind and solar farms, expertly dispatching them to balance the existing energy system.

The Importance of Widespread AI Usage in Energy

Artificial Intelligence is a powerful tool in the energy sector that assesses the environment and confidently recommends the necessary actions to fully optimize the industry's potential. Utilities are trying to catch up with the global rise in demand and implement AI in energy grids, renewables, and decentralized networks to optimize energy use and improve customer satisfaction. AI integrates with energy and lowers the costs and improves the parity and formulates sustainable practices .

Given that 50% of oil and gas companies have outlined plans to increase investments in AI and ML, it is crucial to partner with a service provider who possesses sufficient expertise. Certain energy network projects may be outdated or operate on specific technological specifications, necessitating the involvement of a dedicated team. A competent development vendor is a must for projects like digital twins, smart grids, failure prediction systems, or software for decentralized networks.